No abstract

The sustained activity of Albugranin in mice and monkeys demonstrated in these studies suggests that this agent could be given less frequently than Filgrastim to achieve similar therapeutic effects in patients.

Data silos are proliferating while research and development activity explode following genetic and immunological advances for many clinically described disorders with previously unknown etiologies. The latter event has inspired optimism in the patient, clinical, and research communities that disease-specific treatments are on the way. However, we fear the tendency of various stakeholders to balkanize databases in proprietary formats, driven by current economic and academic incentives, will inevitably fragment the expanding knowledge base and undermine current and future research efforts to develop much-needed treatments. The proliferation of proprietary databases, compounded by a paucity of meaningful outcome measures and/or good natural history data, slows our ability to generate scalable solutions to benefit chronically underserved patient populations in ways that would translate to more common diseases. The current research and development landscape sets too many projects up for unnecessary failure, particularly in the rare disease sphere, and does a grave disservice to highly vulnerable patients. This system also encourages the collection of redundant data in uncoordinated parallel studies and registries to ultimately delay or deny potential treatments for ostensibly tractable diseases; it also promotes the waste of precious time, energy, and resources. Groups at the National Institutes of Health and Food and Drug Administration have started programs to address these issues. However, we and many others feel there should be significantly more discussion of how to coordinate and scale registry efforts. Such discourse aims to reduce needless complexity and duplication of efforts, as well as promote a pre-competitive knowledge ecosystem for rare disease drug development that cultivates and accelerates innovation.

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Background The understanding of the impact of COVID‐19 in patients with cancer is evolving, with need for rapid analysis. Aims This study aims to compare the clinical and demographic characteristics of patients with cancer (with and without COVID‐19) and characterize the clinical outcomes of patients with COVID‐19 and cancer. Methods and Results Real‐world data (RWD) from two health systems were used to identify 146 702 adults diagnosed with cancer between 2015 and 2020; 1267 COVID‐19 cases were identified between February 1 and July 30, 2020. Demographic, clinical, and socioeconomic characteristics were extracted. Incidence of all‐cause mortality, hospitalizations, and invasive respiratory support was assessed between February 1 and August 14, 2020. Among patients with cancer, patients with COVID‐19 were more likely to be Non‐Hispanic black (NHB), have active cancer, have comorbidities, and/or live in zip codes with median household income <$30 000. Patients with COVID‐19 living in lower‐income areas and NHB patients were at greatest risk for hospitalization from pneumonia, fluid and electrolyte disorders, cough, respiratory failure, and acute renal failure and were more likely to receive hydroxychloroquine. All‐cause mortality, hospital admission, and invasive respiratory support were more frequent among patients with cancer and COVID‐19. Male sex, increasing age, living in zip codes with median household income <$30 000, history of pulmonary circulation disorders, and recent treatment with immune checkpoint inhibitors or chemotherapy were associated with greater odds of all‐cause mortality in multivariable logistic regression models. Conclusion RWD can be rapidly leveraged to understand urgent healthcare challenges. Patients with cancer are more vulnerable to COVID‐19 effects, especially in the setting of active cancer and comorbidities, with additional risk observed in NHB patients and those living in zip codes with median household income <$30 000.